Quantifying process tradeoffs in the operation of chromatographic sequences

Analysis of tradeoffs between purification factor and yield for high-performance countercurrent membrane purification for protein separations

High-performance countercurrent membrane purification (HPCMP) has recently been presented as a new approach for protein separations, exploiting differences in diffusive transport across a semipermeable membrane to achieve high selectivity for protein separations. This study presents a set of design equations and diagrams that describe the tradeoff between the yield and purification factor in HPCMP processes in terms of two parametric variables: the diffusive membrane selectivity and the ratio of the draw to bulk solution flow rates. Conditions are identified that provide the high yields and purification factors of interest in bioprocessing. In addition, hydrodynamic models for solute transport were used to evaluate the selectivity as a function of the membrane pore size distribution for purely size-based separations. Model calculations demonstrate that diffusive transport provides significantly greater selectivity than traditional pressure-driven membrane separations for the same pore size distribution due to differences in hindered transport rates for diffusion and convection. These results provide a framework that can be used for the development of HPCMP processes for highly selective protein separations.

High throughput process development workflow with advanced decision-support for antibody purification

Chromatography remains the workhorse in antibody purification; however process development and characterisation still require significant resources. The high number of operating parameters involved requires extensive experimentation, traditionally performed at small- and pilot-scale, leading to demands in terms of materials and time that can be a challenge. The main objective of this research was the establishment of a novel High Throughput Process Development (HTPD) workflow combining scale-down chromatography experimentation with advanced decision-support techniques in order to minimise the consumption of resources and accelerate the development timeframe. Additionally, the HTPD workflow provides a framework to rapidly manipulate large datasets in an automated fashion. The central component of the HTPD workflow is the systematic integration of a microscale chromatography experimentation strategy with an advanced chromatogram evaluation method, design of experiments (DoE) and multivariate data analysis. The outputs of this are leveraged into the screening and optimisation components of the workflow. For the screening component, a decision-support tool was developed combining different multi-criteria decision-making techniques to enable a fair comparison of a number of CEX resin candidates and determine those that demonstrate superior purification performance. This provided a rational methodology for screening chromatography resins and process parameters. For the optimisation component, the workflow leverages insights provided through screening experimentation to guide subsequent DoE experiments so as to tune significant process parameters for the selected resin. The resulting empirical correlations are linked to a stochastic modelling technique so as to predict the optimal and most robust chromatographic process parameters to achieve the desired performance criteria.

Optimal synthesis of chromatographic trains for downstream protein processing

Downstream bioprocessing and especially chromatographic steps, commonly used for the purification of multicomponent systems, are significant cost drivers in the production of therapeutic proteins. There has been an increased interest in the development of systematic methods for the design of such processes, and the appropriate selection of a series of chromatographic steps is still a major challenge to be addressed. Several models have been developed previously but have assumed that 100% recovery of the desired product is obtained at each chromatographic step. In this work, a mathematical framework is proposed, based on mixed integer optimisation techniques, that removes this assumption and allows full flexibility on the position of retention time cut-points, between which the desired product fraction is collected. The proposed model is demonstrated on three example protein mixtures, each containing up to 13 contaminants and selecting from a set of up to 21 candidate steps. The proposed model results in a reduction of one to three chromatographic steps over solutions that no losses are allowed.